Multi-omics Data Integration, Interpretation, and Its Application

Subramanian, Indhupriya; Verma, Srikant; Kumar, Sanjiv; Jere, Abhay; Anamika, Krishanpal

doi:10.1177/1177932219899051

Cited by 900 publications

(679 citation statements)

References 105 publications

(221 reference statements)

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“…a full transcriptome) [33]. This type of multi-omics analysis tends to be consensus-driven [34], a critical approach to reduce the perennial issue of false discovery in data-driven studies, where the number of response variables p typically far exceeds the number of observations n (i.e. p ≫ n).…”

Section: Data-driven Approaches To Non-consensus Network and Causal Inmentioning

confidence: 99%

Multi-Omic Profiling of the Liver Across Diets and Age in a Diverse Mouse Population

Williams

Pfister

Roy

et al. 2020

Preprint

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Systems biology approaches often use networks of gene expression and metabolite data to identify regulatory factors and pathways connected with phenotypic variance. Separating upstream causal mechanisms, downstream biomarkers, and incidental correlations remains a significant challenge, yet it is essential for designing mechanistic experiments. To address this, we first designed a population following 2157 individual mice from 89 isogenic strains of BXD mice across their lifespans to identify molecular interactions between genotype, environment, age (GxExA) and metabolic fitness. Each strain was separated into two cohorts, fed low fat (6% cal/fat) or high fat (60% cal/fat) diets. One-third of individuals (662) were sacrificed at ~6, 12, 18, or 24 months-of-age, with the remainder monitored until natural death. Transcriptome, proteome, and metabolome profiles were generated from liver samples. These multi-omic measurements were deconvolved into metabolic networks, where we observed varying network connectivity as a function of GxExA. The multiple independent study variables permitted causal inference analysis for the network variants using stability selection. This calculates the strength and directionality of the interactions between molecular measurements and metabolic networks as a function of age, diet, and genotype, and assigns each gene a score for its relative position to the target pathway. At 1% FDR, 94% of novel connections were stable across age and diet, such as the connection between Rdh11 with cholesterol biosynthesis and Mut with mitochondrial translation. 6% of discovered candidate genes were unstable, indicating a clear causal relationship between the segregating independent variable, the gene, and the pathway. For instance, age drives variation in proteasomal genes (e.g. Psmb3, Psmb4), which in turn drive changes in the mitochondrial ribosome. Conversely, COX7A2L malformation drives variation in OXPHOS genes, but both are downstream of changes in mitochondrial translation. Finally, we examined all data for connections with the longevity and known longevity-related pathways, identifying several dozen novel candidate genes. Specific C. elegans orthologs for the top two candidates, Ctsd and St7, were knocked down with RNAi and found to reduce longevity both in wildtype worms and in mutant long-lived strains.

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Section: Data-driven Approaches To Non-consensus Network and Causal Inmentioning

confidence: 99%

Multi-Omic Profiling of the Liver Across Diets and Age in a Diverse Mouse Population

Williams

Pfister

Roy

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…MOFA allows a variety of downstream analyses, including sample subgroup identification, data imputation and outlier detection. It was applied to a cohort of patients with chronic lymphocytic leukemia (Subramanian et al, 2020). It identified the main factors of variability between patients, which improved the interpretation of data and facilitated the definition of predictive models of clinical outcomes.…”

Section: Feature Selection Methodsmentioning

confidence: 99%

Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

Labory

Fierville

Ait-El-Mkadem

et al. 2020

Front. Mol. Biosci.

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Mitochondrial diseases (MD) are rare disorders caused by deficiency of the mitochondrial respiratory chain, which provides energy in each cell. They are characterized by a high clinical and genetic heterogeneity and in most patients, the responsible gene is unknown. Diagnosis is based on the identification of the causative gene that allows genetic counseling, prenatal diagnosis, understanding of pathological mechanisms, and personalized therapeutic approaches. Despite the emergence of Next Generation Sequencing (NGS), to date, more than one out of two patients has no diagnosis in the absence of identification of the responsible gene. Technologies currently used for detecting causal variants (genetic alterations) is far from complete, leading many variants of unknown significance (VUS) and mainly based on the use of whole exome sequencing thus neglecting the identification of non-coding variants. The complexity of human genome and its regulation at multiple levels has led biologists to develop several assays to interrogate the different aspects of biological processes. While one-dimension single omics investigation offers a peek of this complex system, the combination of different omics data allows the discovery of coherent signatures. The community of computational biologists and bioinformaticians, in order to integrate data from different omics, has developed several approaches and tools. However, it is difficult to understand which suits the best to predict diverse phenotypic outcome. First attempts to use multi-omics approaches showed an improvement of the diagnostic power. However, we are far from a complete understanding of MD and their diagnosis. After reviewing multi-omics algorithms developed in the latest years, we are proposing here a novel data-driven classification and we will discuss how multi-omics will change and improve the diagnosis of MD. Due to the growing use of multi-omics approaches in MD, we foresee that this work will contribute to set up good practices to perform multi-omics data integration to improve the prediction of phenotypic outcomes and the diagnostic power of MD.

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“…The objectives of nutri-OMICs studies are to report: 1. the variations in nutrients and OMICs; and/or 2. the correlations and interactions between the variables or factors to draw conclusions, from the observational or experimental studies. The requirement for advanced analytical strategies is needed to adapt to the recent access to OMICs and multi-OMICs data [ 69 ]. To our knowledge, only 16 studies have integrated nutrition data (such as food frequency or dietary recall) with multi-OMICs data (see Table 1 : Multi-OMICs in nutrition research) to explain the molecular mechanisms of diets and food supplements on health.…”

Section: Multi-omics Studies In Nutrition Researchmentioning

confidence: 99%

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

et al. 2020

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Nutritional compounds may have an influence on different OMICs levels, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and metagenomics. The integration of OMICs data is challenging but may provide new knowledge to explain the mechanisms involved in the metabolism of nutrients and diseases. Traditional statistical analyses play an important role in description and data association; however, these statistical procedures are not sufficiently enough powered to interpret the large integrated multiple OMICs (multi-OMICS) datasets. Machine learning (ML) approaches can play a major role in the interpretation of multi-OMICS in nutrition research. Specifically, ML can be used for data mining, sample clustering, and classification to produce predictive models and algorithms for integration of multi-OMICs in response to dietary intake. The objective of this review was to investigate the strategies used for the analysis of multi-OMICs data in nutrition studies. Sixteen recent studies aimed to understand the association between dietary intake and multi-OMICs data are summarized. Multivariate analysis in multi-OMICs nutrition studies is used more commonly for analyses. Overall, as nutrition research incorporated multi-OMICs data, the use of novel approaches of analysis such as ML needs to complement the traditional statistical analyses to fully explain the impact of nutrition on health and disease.

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Multi-omics Data Integration, Interpretation, and Its Application

Cited by 900 publications

References 105 publications

Multi-Omic Profiling of the Liver Across Diets and Age in a Diverse Mouse Population

Multi-Omic Profiling of the Liver Across Diets and Age in a Diverse Mouse Population

Multi-Omics Approaches to Improve Mitochondrial Disease Diagnosis: Challenges, Advances, and Perspectives

Statistical and Machine-Learning Analyses in Nutritional Genomics Studies

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